Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms

By Mikkel T. Jensen. IEEE Transactions on Evolutionary Computation, 2003

486 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GDE3: the third evolution step of generalized differential evolution (2005, methodology): “Instead of clustering, niching is performed using an approximate distance measure, Crowding Distance, which can be calculated in time 0 (MN log N) [18].”
An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization (2015, methodology): “A recursive nondominated sorting approach [10], usually called Jensen’s sort, was suggested based on the divide-and-conquer mechanism, which reduces the time com-”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, unknown): “More efcient data-structures that can be used to speed up the niching, selection and archiving processes that these MOEAs rely upon are also now available [ 32 ].”
Multi-objective optimization using genetic algorithms: A tutorial (2006, methodology): “Multi-objective GA use two strategies to implement elitism [26]: (i) maintaining elitist solutions in the population, and (ii) storing elitist solutions in an external secondary list and reintroducing them to the population.”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “The nondominated sorting (line 4 in Algorithm 2) has a time complexity of O(Nlogm−2N) [47] in terms of the number of comparisons.”